Short Abstract:The tutorial will focus on recent advances in sensors, circuits and systems for new generations of vehicles, with driver-assisted/autonomous capability, and smart mobility systems. The social and economic impact of the smart transportation field is huge since every year 90 millions of vehicles are sold worldwide and 1.25 millions of people are killed due to lack of safety. In US 3.1 billions of gallons of fuel are wasted each year due to traffic congestion. Assisted driving, and in the next future autonomous driving, will increase safety, and will enable intelligent management of traffic flows. Key enabling technologies for this scenario are advanced sensors (including Radar, camera, Lidar and inertial sensors), and relevant acquisition and signal processing systems, V2X (vehicle to everything) communication, and on-bard sensor fusion in real-time and with high functional safety levels. During the first day, the course will host the Guest lecture of Dr. Fabrizio Gagliardi entitled “Considerations on Machine-Learned Automated Decision Making”.

Course Contents in brief:The tutorial will be divided in 4 parts.In Part 1 innovation and market trends in the field of electronics and ICT (Information and Communication Technology), applied to new generations of vehicles and mobility systems, will be discussed. Automotive operating requirements in terms of ESD (ElectroStaticDischarge), temperature range, over-voltage/current protection and integrated diagnostic, will be discussed too.In Part 2 real-time acquisition and processing circuits/systems for advanced sensors, including Radar and Lidar, will be presented. These sensors aim at detecting if there are obstacles around the vehicle, and at measuring their distance, relative speeds, and directions.Part 3 of the tutorial will focus on vision sensors, organized as an array of video cameras operating in visible or infrared spectrum. The problem of reducing the distortions caused by the adoption of large Field of View fish eye lens will be also discussed. Some applications to traffic sign recognition systems, road signs recognition, image mosaicking for all around view during parking assistance, will be discussed.Part 4 will discuss examples of driver assistance/autonomous navigation by using data fusion, i.e. integrating information coming from Radar and Lidar and video camera sensors, or from on-board MEMS inertial sensors, or acquired through V2X wireless systems (like satellite positioning/navigation or IEEE 802.11p vehicular networks).During the first day, the course will host the Guest lecture of Dr. Fabrizio Gagliardi entitled “Considerations on Machine-Learned Automated Decision Making”.